{"created":"2021-10-29T00:53:23.139932+00:00","id":2001713,"links":{},"metadata":{"_buckets":{"deposit":"05703478-679b-4fab-9ec5-a2e7cdf2c01d"},"_deposit":{"created_by":25,"id":"2001713","owner":"25","owners":[25],"owners_ext":{"displayname":"wataka","username":"kuma1008"},"pid":{"revision_id":0,"type":"depid","value":"2001713"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:02001713","sets":["152:7995","3:62:5587:1635468665736"]},"author_link":["203866"],"control_number":"2001713","item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"435","bibliographicPageStart":"405","bibliographicVolumeNumber":"28","bibliographic_titles":[{"bibliographic_title":"Evolutionary Computation","bibliographic_titleLang":"en"}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the default CMA-ES with the advantages of separable CMA-ES without inheriting its drawbacks. Technically, we introduce a diagonal matrix D\n\nthat expresses coordinate-wise variances of the sampling distribution in DCD form. The diagonal matrix can learn a rescaling of the problem in the coordinates within a linear number of function evaluations. Diagonal decoding can also exploit separability of the problem, but, crucially, does not compromise the performance on nonseparable problems. The latter is accomplished by modulating the learning rate for the diagonal matrix based on the condition number of the underlying correlation matrix. dd-CMA-ES not only combines the advantages of default and separable CMA-ES, but may achieve overadditive speedup: it improves the performance, and even the scaling, of the better of default and separable CMA-ES on classes of nonseparable test functions that reflect, arguably, a landscape feature commonly observed in practice.\n\nThe article makes two further secondary contributions: we introduce two different approaches to guarantee positive definiteness of the covariance matrix with active CMA, which is valuable in particular with large population size; we revise the default parameter setting in CMA-ES, proposing accelerated settings in particular for large dimension.\n\nAll our contributions can be viewed as independent improvements of CMA-ES, yet they are also complementary and can be seamlessly combined. In numerical experiments with dd-CMA-ES up to dimension 5120, we observe remarkable improvements over the original covariance matrix adaptation on functions with coordinate-wise ill-conditioning. The improvement is observed also for large population sizes up to about dimension squared.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MIT Press","subitem_publisher_language":"en"}]},"item_5_relation_10":{"attribute_name":"PubMed番号","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"31120772","subitem_relation_type_select":"PMID"}}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1162/evco_a_00260","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2019 Massachusetts Institute of Technology","subitem_rights_language":"en"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1063-6560","subitem_source_identifier_type":"PISSN"}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA10913479","subitem_source_identifier_type":"NCID"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"秋本, 洋平","creatorNameLang":"ja"},{"creatorName":"アキモト, ヨウヘイ","creatorNameLang":"ja-Kana"},{"creatorName":"AKIMOTO, Yohei","creatorNameLang":"en"}],"familyNames":[{},{},{}],"givenNames":[{},{},{}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"Hansen, N.","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-08-10"}],"displaytype":"detail","filename":"EC_28-3.pdf","filesize":[{"value":"3.6 MB"}],"format":"application/pdf","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://tsukuba.repo.nii.ac.jp/record/2001713/files/EC_28-3.pdf"},"version_id":"15508758-c46d-4b32-b9da-85862f3cbf82"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies","subitem_title_language":"en"}]},"item_type_id":"5","owner":"25","path":["7995","1635468665736"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-10-29"},"publish_date":"2021-10-29","publish_status":"0","recid":"2001713","relation_version_is_last":true,"title":["Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies"],"weko_creator_id":"25","weko_shared_id":-1},"updated":"2022-04-27T10:02:00.002868+00:00"}